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- The goal of this dataset is to test deep learning algorithms that predict yearly Above Ground Biomass (AGB) for Finnish forests using satellite imagery.
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- Feature data: Satellite imagery from the European Space Agency and European Commission's joint Sentinel-1 and Sentinel-2 satellite missions, designed to collect a rich array of Earth observation data
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- Label data: Ground-truth AGB measurements collected using LiDAR (Light Detection and Ranging) calibrated with in-situ measurements. LiDAR is able to generate high-quality AGB maps, but is more time consuming and intensive to collect than satellite imagery.
 
 
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+ # BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series [https://nascetti-a.github.io/BioMasster/]
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+ The objective of this repository is to provide a deep learning ready dataset to predict yearly Above Ground Biomass (AGB) for Finnish forests using multi-temporal satellite imagery from
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+ the European Space Agency and European Commission's joint Sentinel-1 and Sentinel-2 satellite missions, designed to collect a rich array of Earth observation data
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+ ### Reference data:
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+ * Reference AGB measurements were collected using LiDAR (Light Detection and Ranging) calibrated with in-situ measurements.
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+ * Total 13000 patches, each patch covering 2,560 by 2,560 meter area.
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+ ### Feature data:
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+ * Sentinel-1 SAR and Sentinel-2 MSI data
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+ * 12 months of data (1 image per month)
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+ * Total 310,000 patches
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+
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+ ### Data Specifications:
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+ ![img](./Data_specifications.png)
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+
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+ ### Data Size:
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+
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+ ```
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+ dataset | # files | size
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+ --------------------------------------
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+ train_features | 189078 | 215.9GB
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+ test_features | 63348 | 73.0GB
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+ train_agbm | 8689 | 2.1GB
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+ ```
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+
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+ ## Citation : under review